Text analysis is the ability to leverage AI to analyze automatically all text/unstructured data in your Feedier account.
The module offer different features out of the box, such as:
Automatically generating summaries
Automatically detecting new topics
Tracking and analyzing topics (with sentiment analysis and link to attributes)
Follow topic trends
Leverage Feedier’s filtering technology as in the Dashboards, Reports, Segments, etc.
How to set up Text Analysis?
Text analysis must be enabled from an administrator user in the “Feature access“ page.
Data is analyzed by the Text Analysis module
The module is automatically using all text/unstructured data present in the teams the user has access too. So, as long as the account has open text items, Text analysis will bring value and show automatically new topics and measure sentiment.
Sentiment analysis in Feedier
The sentiment score is a quantitative metric that is any number between 0 and 100, where 0 typically indicates extremely negative sentiment, 100 represents extremely positive sentiment, and values in between reflect varying degrees of neutrality or mixed emotions. This score is widely used to evaluate the emotional tone in textual data.
The sentiment score of a topic = total average of sentiment scores of verbatim related to this topic.
Sentiment | Explanation |
61 to 100 | The verbatim is linked to a positive emotion on the part of the customer: Joy, Confidence, Serenity, Admiration. |
41 to 60 | The verbatim is not directly linked to an emotion or the emotion is not expressed strongly enough to be categorised. |
1 to 40 | The verbatim is linked to a negative emotion on the part of the customer: Anger, Contempt, Sadness, Disgust. |
For the sake of the simplicity, the score is displayed with a label:
Positive From 61 to 100
Neutral From 41 to 60
Negative From 1 to 40
Topics in Feedier at the core of the Text analysis
A topic in the Feedier Platform is a way to group different answers/text based/verbatims based on a common meaning.
For exemple here, Delivery Issues is a Topic that regroups all feedbacks related to a problem with the delivery. In the same way, the Pricing Issues allows to focus only on feedbacks where the price is mentioned negatively.
How to create a topic in Feedier
Two ways of automatically creating topics in Feedier :
Automatically suggested topic with the AI
2. Manually create a topic
Step 1 : Defining the topic
Giving the right instructions when creating a topic
To improve the topic precisions it is important to give here a proper explanation of the topic. You can explain the topic here in simple terms for someone not expert in your field.
Step 2 : Reviewing verbatims
Here you can detach the verbatims if you realise that it is not coherent with your topic.
Features on Topics :
Get AI insights on a topic
Here you can detect positive and negative aspects of the topic and directly trigger action plan on negative aspects.
2. Breakdown by Attributes
You can breakdown the topic by attribute to have comprehensive view for your organisation
3. Explore the verbatims related to the topic
KPIs on Topics :
1. Sentiment score
Is the total average of sentiment scores of verbatim related to a topic.
Positive → 61 to 100
Neutral → 40 to 60
Negative → 1 to 39
Topic Precision Score
The Topic Precision Score shows how accurately our AI is assigning responses to a topic.
It helps you:
Understand how well the AI is performing in terms of matching
Identify when responses are matched incorrectly
Improve accuracy over time by manually attaching or detaching responses
From Text Analysis to actionable Insights
From global to specific
The main important feature of the Text Analysis module is the ability to filter in real-time all the insights based on your specific context (time frame, attributes, source, team, etc.).
How to Detect pain points in the Text Analysis
Two elements are extremely important when it comes to pain points in Text Analysis:
The painpoint itself
Its impact on the business
Feedier helps you do to both in one place.
Identifying pain points
There are two different methods:
Use global topic names (such as product, quality, service, sales, etc.) and add a sentiment score filter (positive or negative) to only match outliers in the Text analysis module.
Use specific topic names (such as product bugs, product ideas, service issues, sales bottlenecks) to only match outliers.
Indentifying root cause
When it comes to root cause, the attributes related to the topics are the most actionable insights.
How to find actions to tackle pain points : Generate Action Plan
Action plans with key improvement and pain points can be generated for every single topic by Feedier.
You can either create a global action plan or focusing on a specific negative aspects.